similar to: [R-pkgs] heplots 0.9-3 and candisc 0.5-18 released to CRAN

Hello, I am having trouble loading the package candisc onto my R distribution. I am using 2.7.1-2. I do a "> install.packages("candisc" and get the following output. Warning in install.packages("candisc") : argument 'lib' is missing: using '/usr/local/lib/R/site-library' --- Please select a CRAN mirror for use in this session --- Loading Tcl/Tk

[Env: Tested under Win Xp, R 2.9.2 and R 2.10.1; sessionInfo() at end] I've run into a problem with the heplot3d() function in my heplots package which causes the R GUI to crash ('R for Windows GUI encountered a problem and needs to close...'). I think the problem comes from an rgl call, but, I can't get a traceback or other information because my R session crashes. I've

I'm happy to announce the candisc package, v 0.5-9, now on CRAN. Generalized Canonical Discriminant Analysis Description This package includes functions for computing and visualizing generalized canonical discriminant analyses for a multivariate linear model. They are designed to provide low-rank visualizations of terms in a mlm via the plot method and the heplots package. The methods

I'm happy to announce the candisc package, v 0.5-9, now on CRAN. Generalized Canonical Discriminant Analysis Description This package includes functions for computing and visualizing generalized canonical discriminant analyses for a multivariate linear model. They are designed to provide low-rank visualizations of terms in a mlm via the plot method and the heplots package. The methods

[Using R 2.7.2 on Windows XP] After re-building our heplots package, I've begun to get the following error from sessionInfo(), even though it passes R CMD check and builds without errors: > sessionInfo() Error in x$Priority : $ operator is invalid for atomic vectors In addition: Warning message: In FUN(c("MASS", "heplots", "car", "rgl",

I'm working on some functions for generalized canonical discriminant analysis in conjunction with the heplots package. I've written a candisc.mlm function that takes an mlm object and computes a candisc object containing canonical scores, coeficients, etc. But I'm stumped on how to construct a mlm for the canonical scores, in a function using the *same* right-hand-side of the model

Running: R Version 2.3.1 (2006-06-01), debian stable I'm testing an application that uses rgl (installed with apt-get install r-cran-rgl) but fails, at least partly because that application uses a more current version. There is also a mysterious error msg from Xlib: > library(car) > library(rgl) > iris.mod <- lm(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) ~

Hi, I am doing canonical discriminant analysis using candisc function from the candisc package. My input is a table of species distribution (columns = abundance of each species in each sample) in samples that are split by categories (rows), and I want to know whether each category is associated with a particular set of species and their abundances. I have 20 rows (samples) split into 6

Hi listers, I am working on an canonical discriminant analysis, but I am having some trouble to use the CANDISC function... I just installed the last R version and installed the package CANDISC... But, I am getting the following message because about a permission: The downloaded packages are in C:\Users\Marcio\AppData\Local\Temp\Rtmpz2kFUm\downloaded_packages updating HTML package descriptions

Hello, I have a question regarding the candisc package. My data are: species three five 1 2.95 6.63 1 2.53 7.79 1 3.57 5.65 1 3.16 5.47 2 2.58 4.46 2 2.16 6.22 2 3.27 3.52 I put these in a table and then a linear model >newdata <- lm(cbind(three, five) ~ species, data=rawdata) and then do a candisc on them >candata<-candisc(newdata)

Hello, I have a file with two dependent variables (three and five) and one independent variable. I do i.mod <- lm(cbind(three, five) ~ species, data=i.txt) and get the following output: Coefficients: three five (Intercept) 9.949 9.586 species -1.166 -1.156 I do a" i.can<-candisc(i.mod,data=i): and get the following output: Canonical Discriminant Analysis

This may be elementary, but I can't find an answer: How can I set up global options for some specific arguments to functions in a package which can be easily changed by the user? This question relates to the selection of colors used in functions in several packages (heplots, genridge), where I want to provide reasonable default values for plots, but allow users to change those defaults

[Using R 2.8.0 / Win XP / ] I just added a CITATION file to the heplots package--- appended below. From the document ion for ?CITATION, there can be *one or more* calls to citEntry() within the CITATION file, and each should produce an object of class "citation". With just a single citEntry(), citation produces the expected output, with BibTeX entry: >

Env: R 2.8.1, Win Xp, Eclipse/StatET In a .Rd file, I have an example containing the lines: # calculate Y M, using polynomial contrasts trends <- as.matrix(VocabGrowth) %*% poly(8:11, degree=3) colnames(trends)<- c("Linear", "Quad", "Cubic") [At the risk of a long message, I'll append the complete .Rd file at the end of this message, in case this was

[Env: Win Xp / StatET 2.0 / R 2.15.0] In my heplots package I extended the HE-examples.Rnw vignette under inst/doc. The package passes R CMD check on my machine: * using log directory 'C:/eclipse-3.7/heplots.Rcheck' * using R version 2.15.0 (2012-03-30) * using platform: i386-pc-mingw32 (32-bit) ... * checking sizes of PDF files under 'inst/doc' ... OK * checking installed files

[Env: R 2.14.2 / Win Xp] In the examples below, I'm using lattice::bwplot to plot boxplots of 4 variables, grouped by a factor 'epoch' which also corresponds to a numeric year. I'd like to modify the plots to position the boxplots according to the numeric value of year, but I can't figure out how to do this. Also, I'd to modify the strip labels that give the variable

I'm trying to write a formula method for canonical correlation analysis, that could be called similarly to lm() for a multivariate response: cancor(cbind(y1,y2,y3) ~ x1+x2+x3+x4, data=, ...) or perhaps more naturally, cancor(cbind(y1,y2,y3) ~ cbind(x1,x2,x3,x4), data=, ...) I've adapted the code from lm() to my case, but in this situation, it doesn't make sense to include an

A new function in my heplots package wants to use a non-exported utility function in the car package, df.terms, but this depends on other non-exported functions. From the Writing R extensions manual, I thought I could do this via (in my NAMESPACE) importFrom(car, car:::df.terms, car:::df.terms.default, car:::is.aliased) but I get: ** preparing package for lazy loading Error : objects

I added a new data file, NLSY.RData, to a package that uses LazyData: Yes It passed R CRAN check and R CMD install worked w/o significant complaints. * installing to library 'C:/R/R-2.15.2/library' * installing *source* package 'heplots' ... ** R ** data ** moving datasets to lazyload DB ** demo ** inst ** preparing package for lazy loading Warning: package 'nnet' was

Dear R gurus, I'm trying to solve what I assume is a fairly simple problem, but I'm having trouble finding the proper approach. I have a matrix where each column is some object (e.g. a car) and each row is a numeric measurement of a feature of said object (e.g. horse power, top speed, etc.). Let's also suppose that I know what make the car is (e.g. toyota, ford, etc.), stored in a